Understanding Quartiles: Different Methods for Calculating and Their Purpose

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In summary, the conversation discusses different methods for finding quartiles and how they can result in different values. The purpose of finding quartiles is to understand the distribution of data and identify the middle values. Ultimately, all methods are considered correct and the value of the quartiles may not be as important as understanding the overall distribution of the data.
  • #1
songoku
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TL;DR Summary
Let say I have 21 data, from 1 to 21

What is the upper quartile?
If I do it manually, this is what I do:

1) find the median (which is 11)

2) find the middle data of the "upper data" (data to the right of median), which will be the middle between 16th and 17th data:
$$\frac{16+17}{2}=16.5$$But I got this note from my teacher:
1622037030666.png


Using that method:
1) find ##\frac 3 4## of n, which is 15.75

2) round up ##\frac{3}{4}n## , which is 16

3) pick 16th data, which is 16I get two different results. Which one is correct?

Thanks
 
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  • #2
This Wikipedia page describes four different ways to calculate quartiles.
 
  • #3
Mark44 said:
This Wikipedia page describes four different ways to calculate quartiles.
So all methods are correct eventhough the results are different? If yes, it means that the value of the quartiles are not really important, so what is the purpose of finding quartiles?

Thanks
 

Related to Understanding Quartiles: Different Methods for Calculating and Their Purpose

1. What is the definition of quartiles?

Quartiles are values that divide a dataset into four equal parts. The first quartile (Q1) is the value that separates the bottom 25% of the data from the top 75%. The second quartile (Q2) is the median, which divides the data into two equal parts. The third quartile (Q3) separates the bottom 75% of the data from the top 25%.

2. How do you calculate quartiles?

To calculate quartiles, you first need to arrange the data in ascending order. Then, find the median of the entire dataset, which will be the second quartile (Q2). To find the first quartile (Q1), find the median of the lower half of the data (values below Q2). To find the third quartile (Q3), find the median of the upper half of the data (values above Q2).

3. What is the purpose of quartiles?

Quartiles are used to divide a dataset into four equal parts and provide a measure of variability or spread in the data. They can also help identify outliers or extreme values in a dataset.

4. Can quartiles be used with any type of data?

Yes, quartiles can be used with any type of numerical data, including whole numbers, decimals, and fractions. However, they are most commonly used with continuous data, such as measurements or test scores.

5. How are quartiles related to box plots?

Quartiles are used to create box plots, which are graphical representations of a dataset's quartiles. The box in a box plot represents the middle 50% of the data, with the median (Q2) marked by a line inside the box. The lower and upper quartiles (Q1 and Q3) are represented by the edges of the box. The "whiskers" of the box plot extend to the lowest and highest values in the dataset, with any outliers shown as individual points beyond the whiskers.

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